248 citations · 343 across the 7 of their papers we have counts for
7 papers
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
Chengliang Liu, Jie Wen, Xiaoling Luo +1
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view…
Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification
Chengliang Liu, Jie Wen, Yong Xu +3
As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of mult…
A Survey on Incomplete Multi-view Clustering
Jie Wen, Zheng Zhang, Lunke Fei +4
Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, suc…
Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification
Zhijun Hu, Yong Xu, Jie Wen +4
In this paper, we propose a Global-Supervised Contrastive loss and a view-aware-based post-processing (VABPP) method for the field of vehicle re-identification. The traditional sup…
Vehicle Re-identification Based on Dual Distance Center Loss
Zhijun Hu, Yong Xu, Jie Wen +2
Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the…
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task
Weida Yang, Xindong Ai, Zuliu Yang +2
To improve the performance in ill-posed regions, this paper proposes an atrous granular multi-scale network based on depth edge subnetwork(Dedge-AGMNet). According to a general fac…
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
Jie Wen, Zheng Zhang, Yong Xu +1
Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specifically, the proposed meth…